With today's dramatic expansion of the application range of information technology, it has become necessary to deal with uncertain and inexplicit information that had been hard for computers to process. For example, consider a recommendation system for Internet shopping, in which customer needs are read out from previous transaction history or customer management information, and the most appropriate information is recommended to a customer concerned. Although a customer list may be searched for the closest purchase pattern for the customer concerned, not all customers of the same age and sex necessarily have the same preferences. Also, a customer does not necessarily keeps on having the same preferences as those answered in a questionnaire submitted by the customer at the time of registration. Therefore, it becomes necessary to predict the preferences by comprehensive judgment from the customer's actions (e.g., WWW browsing history), attributes, and questionnaire information. However, this does not always result in a single answer. There may be a plurality of recommendation candidates at the same time. In addition, these candidates are naturally treated as having vagueness and uncertainty, such as “judging from the previously browsed WWW pages, the user is likely to have an interest in football-related information.”
For these problems, a probabilistic framework is effective. A plurality of candidates may each be assigned the degree of certainty, such as the likelihood of having an interest in football-related items being 60% and the likelihood of having an interest in travel being 30%, so that the candidates may be treated with uncertainty. If the previously viewed page was a page about Korea for example, the probability of interest may be calculated for each of the World Cup football-related information, travel-related information, and cooking-related information. Then, information with the highest probability that the customer concerned has an interest therein may be provided. To calculate this probability, many different factors (e.g., a hobby answered in a questionnaire) may be taken into consideration to utilize the dependencies between them (e.g., if the hobby is sport, it is likely that football is of interest). In this manner, more accurate prediction is possible.
As an information processing model for calculating the probability based on such dependencies between a plurality of factors, the Bayesian network has been attracting attention in various fields recently. The Bayesian network is a network-shaped probability model defined by the following three items: (1) random variables, (2) conditioned dependencies between the random variables, and (3) their conditional probability. The item (1) is represented by nodes, and (2) is represented by directed links established between the nodes. A node to which a link is directed is called a child node, and a node from which a link originates is called a parent node. The item (3) is a conditional probability that a child node has a certain value when its parent nodes have certain values. For discrete variables, this is expressed in the form of a table (a conditional probability table) that lists respective probability values for all states that the child node and the parent nodes assume, such as P (child node=y|parent nodes=x1, x2, . . . )=p. (Yoich Motomura, “Bayesian Net Software”, Transactions of the Japanese Society for Artificial Intelligence, Vol. 17, No. 5, a (2002))
The above-described recommendation system may be implemented using the Bayesian network. Specifically, the relationships between attributes etc. of customers and objects of high interest of the customers are represented as a Bayesian network model based on statistical data, such as a questionnaire research result and purchase history obtained from a large number of customers varying in age, sex, lifestyle, and so forth. Then, the Bayesian network model is used to reason out an object of high interest of a customer from the customer's attributes and the situation, and the object of high interest is recommended to the customer based on the reasoning result.
In this recommendation system, the Bayesian network model is generated based on the statistical data obtained from various customers. Therefore, it is considered that the conditions for determining the object to be recommended, such as the customer attributes and the situation, have a small influence on the recommendation. For example, if the condition for determining the object to be recommended is “customer's attribute: father”, data obtained from customers having the attribute “father” is part of the entire statistical data from which the Bayesian network model was generated. The influence of the condition “father” on the model is therefore smaller than that on a model generated based on statistical data obtained only from customers having the attribute “father.” Studies are currently proceeding to sufficiently reflect conditions for determining the recommendation object in the recommendation result and to make more accurate recommendation.
In the light of the above-described background, the present invention aims to provide a vehicle information processing system that allows more appropriately obtaining a recommendation to be provided to a recipient who receives the recommendation.